Science - USA (2021-11-12)

variation to plasma concentrations (Fig. 2D,
fig. S3, and table S3). For about one-third of
the protein targets (n= 1249), >50% of ge-
netic variance was explained by one or more
cis-pQTLs, whereas for 7.2% of the targets (n=
282), protein- or pathway-specific trans-pQTLs
accounted for most of the genetic variation;
this left two-thirds of the targets (n= 2361)
mainly explained by unspecific trans-pQTLs
( 12 ). Overall, we observed a median genetic con-
tribution of 2.7% [interquartile range (IQR), 1.0%
to 7.6%], reaching values above 70% for proteins
such as vitronectin (rs704, MAF = 47.3%) or
sialic acidÐbinding Ig-like lectin 9 (rs2075803,

MAF = 44.1%) which were often driven by only

a single common cis-pQTL. PAVs, which affect

the binding epitope of the protein target, are

the likely explanation for such strong and

isolated genetic effects. Although more than

two-thirds of the protein targets with at least

one cis-pQTL were unrelated to PAVs, we found

that 158 of the protein targets (32.9%) linked

to a PAV (r^2 > 0.6) shared a genetic signal with

at least one disease or risk factor (see below).

This suggests that the conformation and pos-

sibly the function of the protein target, rather

than the plasma abundance of the protein tar-

get, might be more relevant as mediators of

downstream phenotypic consequences, and

that aptamers are able to detect such probably

dysfunctional proteins.

Our approach to identify protein-/pathway-

specific trans-pQTLs allowed us to uncover

biologically relevant information, which was

otherwise hidden by strong and unspecific

trans-pQTLs that possibly interfere with the

measurement technique rather than the biol-

ogy of the protein target. For example, rs704, a

missense variant withinVTNthat associated

with a higher fraction of single-chain vitro-

nectin with altered binding properties ( 19 , 20 )

explained 72% of the variance in MICOS complex

Pietzneret al.,Science 374 , eabj1541 (2021) 12 November 2021 3 of 11

Fig. 2. Classification of protein quantitative trait loci (pQTLs, cis and trans)
and subsequent partition of the explained variance in plasma abundances of
protein targets.(A) Bar chart of pQTL classification based on GO term mapping
(blue) or community mapping in a protein network derived by Gaussian graphical
modeling (GGM; orange) of associated protein targets. Darker shades indicate
cis-pQTLs and lighter colors trans-pQTLs. (B) Data-driven protein network colored
according to 191 identified protein communities. (C) A community-specific pQTL
(PNPLA3) that was not captured by GO term mapping. Gene annotation was as
reported in the supplementary materials. (D) Absolute (top) and relative (bottom)

explained variance in plasma abundances of protein targets by identified pQTLs.

Coloring indicates contribution of the lead cis-pQTL (orange), secondary cis-pQTLs

(yellow), protein- or pathway-specific trans-pQTLs (blue), and unspecific trans-

pQTLs (green). Protein targets have been grouped by underlying genetic architecture

as mostly explained by cis-pQTLs (“cis-determined”), mostly explained by specific

trans-pQTLs (“specific trans”), and mostly explained by unspecific trans-pQTLs

(“unspecific trans”). The inset displays the overall distribution of explained variance by

each of the four categories. The variance explained was computed using linear

regression models. See fig. S3 for a graphical display of effect size distributions.

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